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1.
《Alzheimer's & dementia》2019,15(11):1478-1488
IntroductionPlasma proteins have been widely studied as candidate biomarkers to predict brain amyloid deposition to increase recruitment efficiency in secondary prevention clinical trials for Alzheimer's disease. Most such biomarker studies are targeted to specific proteins or are biased toward high abundant proteins.Methods4001 plasma proteins were measured in two groups of participants (discovery group = 516, replication group = 365) selected from the European Medical Information Framework for Alzheimer's disease Multimodal Biomarker Discovery study, all of whom had measures of amyloid.ResultsA panel of proteins (n = 44), along with age and apolipoprotein E (APOE) ε4, predicted brain amyloid deposition with good performance in both the discovery group (area under the curve = 0.78) and the replication group (area under the curve = 0.68). Furthermore, a causal relationship between amyloid and tau was confirmed by Mendelian randomization.DiscussionThe results suggest that high-dimensional plasma protein testing could be a useful and reproducible approach for measuring brain amyloid deposition.  相似文献   
2.
横断面研究能否进行因果推断   总被引:2,自引:1,他引:1       下载免费PDF全文
基于变量调查(或测量)的共时性、统计学关联及幸存者偏倚等原因,横断面研究被认为不能进行因果推断,这是当前的"共识"。本文基于因果思维,借助因果图,首先明确定义真实截面和测量截面,并识别截面概念仅存在于理论的特性。实际横断面研究中,测量变量的共时性并不存在,而是无一例外地表现为非共时性时序,其实质上相当于测量变量间互为独立性假设,或不存在有差别错分偏倚。类似于累积病例对照研究和历史性队列研究,横断面研究均为暴露和结局已存在或发生后进行的测量,这种测量相当于对变量值的历史重建或"考古"。这类研究进行因果推断的共性前提条件之一是,测量变量与其历史变量间必须存在着因果律。测量变量均为真实变量的替代者,测量变量间的时序在因果推断上并不重要。应加强对横断面研究分析性角色的认识。  相似文献   
3.
Large quantities of data are now available to medical researchers; however, observational studies are plagued by bias and confounding. Additionally, much of this research only speculates on variable associations, leaving prospective randomized clinical trials as the sole purveyors of claims about causal relations between variables. There has been a growing movement of causal inference that uses new techniques to investigate causality using observational data. These techniques include the implementation of directed acyclic graphs, which allow researchers to explicitly and reproducibly define the causal relationships between study variables, thus making statistical analysis more robust. Directed acyclic graphs further allow researchers to identify confounding and other sources of bias and to discover causal effects among complex networks of variables. This review aims to introduce these techniques to the general urology and urologic oncology research communities in order to provide a basic understanding of causal inference and analysis and call for integration of these practices more generally in research methodology.  相似文献   
4.
High-volume hospitals typically perform better than low-volume hospitals. In this paper, we study whether such patterns reflect a causal effect of case volume on patient outcomes. To this end, we exploit closures and openings of entire cancer clinics in Swedish hospitals which provides sharp and arguably exogenous variation in case volumes. Using detailed register data on more than 100,000 treatment episodes of advanced cancer surgery, our results suggest substantial positive effects of operation volume on survival. Complementary analyses point to learning-by-doing as an important explanation.  相似文献   
5.
基于大自然时间轴的测量时序分类法   总被引:2,自引:2,他引:0       下载免费PDF全文
因果推断中,时序(或方向性)的概念尚未完全明确。本文从因果思维出发,以真实因和真实果的发生时间将大自然时间轴划分为3个时域和2个时点,从而锚定了因果推断只能实现于第3时域。测量时序可分为5种类型:跨第1和3时域纵向时序(实验性时序)、跨第2和3时域纵向时序、同时域纵向时序、同时域逆纵向时序和同时域横向时序(观察性时序)。这种分类法适用于首次或多次测量、及时和延后测量等所有测量策略。除了实验中真实因的测量(或干预措施)在其发生之前(第1时域)或观察和实验中真实因的测量在真实果发生之前(第2时域)的情形外,所有其他测量策略类似于历史重建或"考古",测量时序的重要性次于测量的准确性。从研究设计应整合偏倚设计的观点来看,本文提出基于大自然时间轴的测量时序五分类法,概念清楚并将有助于判断研究过程中可能出现的偏倚,为正确进行因果推断研究奠定基础。  相似文献   
6.
虽然预防接种异常反应发生的概率极低,但一旦发生,却常常产生较大的负面社会影响,成为社会普遍关心的问题。本文对有关预防接种负性事件的几个重要定义进行了辨析,包括世界卫生组织(WHO)的定义,我国 “预防接种异常反应”法律定义和 “疑似预防接种异常反应”监测定义。发现我国疑似预防接种异常反应中文名称与英文译名不符,名词之间容易混淆。预防接种异常反应的定义造成在寻求证据和因果关系判定方面存在实际操作困难,需要修改法律定义以放宽证据把握尺度,并借鉴美国“疫苗伤害表”简化因果关系判定标准。建议在更多实证研究获取证据基础上,采用WHO预防接种不良事件概念和定义,并采用无过错责任补偿原则替代《疫苗流通和预防接种管理条例》中对预防接种异常反应的严格法律定义。  相似文献   
7.
Activity spaces are increasingly used to understand how people interact with their environment and engage in activity but their use may raise challenges regarding causal inference. We conducted a systematic review of findings and the methodological, analytical and conceptual issues relevant to causal inference. Studies were included if they comprised a spatial summary of locations visited, assessed any part of the causal pathway between the environment, physical activity and health, and used quantitative or qualitative methods. We searched seven electronic databases in January 2018 and screened 11910 articles for eligibility. Forty-seven studies were included for review. Studies answered research questions about features of or environmental features within activity spaces using a range of spatial and temporal summary techniques. The conceptual challenge of using activity spaces to strengthen causal inference was rarely considered, although some studies discussed circularity, temporality, and plausibility. Future studies should use longitudinal and experimental designs and consider the potential and actual use of spaces for physical activity, and their relationship with total levels of activity.  相似文献   
8.
Abstract

The National Research Council’s report on the prevention and treatment of missing data highlighted the need to clearly specify causal estimands. This focus fundamentally changed how the missing data problem was perceived and addressed in clinical trials. The recent ICH E9(R1) addendum is another major step in promoting the use of the causal estimands framework that should further influence how clinical trial protocols and statistical analysis plans are written and implemented. The language of potential outcomes that is widely accepted in the causal inference literature is not widely recognized in the clinical trialists community and was not used in defining causal estimands in the NRC report or the ICH E9(R1). In this article, we attempt to bridge the gap between the causal inference community and clinical trialists to further advance the use of causal estimands in clinical trial settings. We illustrate how concepts from causal literature, such as potential outcomes and dynamic treatment regimens, can facilitate defining and implementing causal estimands and may provide a unifying language to describing the targets for both observational and randomized clinical trials.  相似文献   
9.
Deprescribing is defined as “the planned and supervised process of dose reduction or stopping of medication that might be causing harm, or no longer be of benefit”. Barriers to deprescribing include healthcare professional fear and lack of guidance. These may stem from limited available evidence on benefits and harms of deprescribing medications commonly used among older persons. Advances in pharmacoepidemiology and causal inference methods to evaluate comparative effectiveness and safety of prescribing medications have yet to be considered for deprescribing medication. This paper discusses select methods and how they can be applied to deprescribing research, using case studies of benzodiazepines and low-dose acetylsalicylic acid (aspirin). Target trial emulation involves the explicit application of design principles from randomised controlled trials to observational studies. Several design aspects, including defining eligibility criteria and time zero, require additional considerations for deprescribing studies. The active comparator new user design also presents challenges, including selection of an appropriate comparator. This paper discusses these aspects, and others, in relation to deprescribing studies. Furthermore, methods proposed to control for confounding, in particular, the prior event rate ratio and propensity scores, are discussed. Introduction of billing codes or mechanisms for accurately determining when deprescribing has occurred would enhance the ability to conduct research using routinely collected data. Although the approaches discussed in this paper may strengthen observational studies of deprescribing, their use may be best suited to certain scenarios or research questions, where randomised controlled trials may be less feasible.  相似文献   
10.
Co-separation studies between surnames and Y chromosome genetic markers are beneficial to revealing population migrations, surname origins, population formation histories and forensic familial searching. Genetic distributions of 27 Y-STRs in Chinese four surnames (Li, Lin, Chen and Huang) from Zhanjiang Han population were investigated. Meanwhile, we tried to develop a decision tree model for surname predictions based on Y-STR haplotypes. Allelic frequencies of 27 Y-STRs showed that unique alleles were only observed in a certain surname; besides, some alleles displayed higher frequencies in a certain surname than those in other surnames, implying these alleles might be employed as the useful indicators for surname predictions. Haplotype match probability values of 27 Y-STRs in these surnames revealed that the system could be used as a valuable tool for forensic male identification. The developed decision tree model performed well for the training set with the accuracy of 0.9860 and obtained the relatively high accuracy (>0.70) for surname predictions of the testing set. To sum up, we explored the power of the machine learning to the surname predictions based on obtained Y-STR haplotypes, which showed promising application values in forensic familial searching.  相似文献   
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